# Single-cell RNA-seq data imputation using Feature Propagation

arXiv: https://arxiv.org/abs/2307.10037 · HTML: https://mlsanity.com/paper/2307.10037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [Training Robust Graph Neural Networks by Modeling Noise Dependencies](https://mlsanity.com/paper/2502.19670/) (2025)

## Abstract (arXiv)

While single-cell RNA sequencing provides an understanding of the transcriptome of individual cells, its high sparsity, often termed dropout, hampers the capture of significant cell-cell relationships. Here, we propose scFP (single-cell Feature Propagation), which directly propagates features, i.e., gene expression, especially in raw feature space, via cell-cell graph. Specifically, it first obtains a warmed-up cell-gene matrix via Hard Feature Propagation which fully utilizes known gene transcripts. Then, we refine the k-Nearest Neighbor(kNN) of the cell-cell graph with a warmed-up cell-gene matrix, followed by Soft Feature Propagation which now allows known gene transcripts to be further denoised through their neighbors. Through extensive experiments on imputation with cell clustering tasks, we demonstrate our proposed model, scFP, outperforms various recent imputation and clustering methods. The source code of scFP can be found at https://github.com/Junseok0207/scFP.